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README.md
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README.md
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<h1 align="center">
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<img src="https://user-images.githubusercontent.com/9354770/171523113-70c7214b-8298-4d7e-abd9-81f5788f6e19.png" alt="Archai logo" width="384px" />
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<br />
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</h1>
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# Archai: Platform for Neural Architecture Search
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<div align="center">
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<b>Archai</b> accelerates your Neural Architecture Search (NAS) through <b>fast</b>, <b>reproducible</b> and <b>modular</b> research, allowing you to generate efficient deep networks for your applications.
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</div>
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[](https://github.com/microsoft/archai/blob/main/LICENSE)
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[](https://github.com/microsoft/archai/issues)
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[](https://github.com/microsoft/archai/releases)
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<br />
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**Archai** is a Neural Network Search (NAS) platform that allows you to generate efficient deep networks for your applications. It offers the following advantages:
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<div align="center">
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<img src ="https://img.shields.io/github/release/microsoft/archai?style=flat-square" alt="Release version" />
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<img src ="https://img.shields.io/github/issues-raw/microsoft/archai?style=flat-square" alt="Open issues" />
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<img src ="https://img.shields.io/github/contributors/microsoft/archai?style=flat-square" alt="Contributors" />
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<img src ="https://img.shields.io/pypi/dm/archai?style=flat-square" alt="PyPI downloads" />
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<img src ="https://img.shields.io/github/license/microsoft/archai?color=red&style=flat-square" alt="License" />
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</div>
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* 🔬 Easy mix-and-match between different algorithms;
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* 📈 Self-documented hyper-parameters and fair comparison;
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* ⚡ Extensible and modular to allow rapid experimentation;
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* 📂 Powerful configuration system and easy-to-use tools.
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<br />
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Please refer to the [documentation](https://microsoft.github.io/archai) for more information.
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Package compatibility: **Python 3.7+** and **PyTorch 1.2.0+**.
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OS compatibility: **Windows**, **Linux** and **MacOS**.
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## Table of contents
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* [Quickstart](#quickstart)
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* [Installation](#installation)
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* [Running an Algorithm](#running-an-algorithm)
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* [Tutorials](#tutorials)
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* [Support](#support)
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* [Contributions](#contributions)
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* [Team](#team)
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* [Credits](#credits)
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* [License](#license)
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* [Trademark](#trademark)
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<div align="center">
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<a href="#quickstart">Quickstart</a> •
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<a href="#installation">Installation</a> •
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<a href="#examples">Examples</a> •
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<a href="#documentation">Documentation</a> •
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<a href="#support">Support</a>
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</div>
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## Quickstart
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### Installation
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To run a specific NAS algorithm, specify it by the `--algos` switch:
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```terminal
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python scripts/main.py --algos darts --full
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```
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Please refer to [running algorithms](https://microsoft.github.io/archai/user-guide/tutorial.html#running-existing-algorithms) for more information on available switches and algorithms.
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## Installation
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There are many alternatives to installing Archai, but note that regardless of choice, we recommend using it within a virtual environment, such as `conda` or `pyenv`.
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#### PyPI
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### PyPI
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PyPI provides a fantastic source of ready-to-go packages, and it is the easiest way to install a new package:
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pip install archai
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```
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#### Source (development)
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### Source (development)
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Alternatively, one can clone this repository and install the bleeding-edge version:
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Please refer to the [installation guide](https://microsoft.github.io/archai/getting-started/install.html) for more information.
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### Running an Algorithm
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To run a specific NAS algorithm, specify it by the `--algos` switch:
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```terminal
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python scripts/main.py --algos darts --full
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```
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Please refer to [running algorithms](https://microsoft.github.io/archai/user-guide/tutorial.html#running-existing-algorithms) for more information on available switches and algorithms.
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### Tutorials
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## Examples
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The best way to familiarize yourself with Archai is to take a quick tour through our [30-minute tutorial](https://microsoft.github.io/archai/user-guide/tutorial.html). Additionally, one can dive into the [Petridish tutorial](https://microsoft.github.io/archai/user-guide/petridish.html) developed at Microsoft Research and available at Archai.
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@ -76,8 +69,14 @@ We highly recommend [Visual Studio Code](https://code.visualstudio.com) to take
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On the other hand, you can use [Archai on Azure](tools/azure/README.md) to run NAS experiments at scale.
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## Documentation
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Please refer to the [documentation](https://microsoft.github.io/archai) for more information.
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## Support
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Archai has been created and maintained by [Shital Shah](https://shital.com), [Debadeepta Dey](www.debadeepta.com), [Gustavo de Rosa](https://www.microsoft.com/en-us/research/people/gderosa), Caio Mendes, [Piero Kauffmann](https://www.microsoft.com/en-us/research/people/pkauffmann/), and [Ofer Dekel](https://www.microsoft.com/en-us/research/people/oferd) at Microsoft Research.
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### Contributions
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This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
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@ -86,20 +85,16 @@ When you submit a pull request, a CLA-bot will automatically determine whether y
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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## Team
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Archai has been created and maintained by [Shital Shah](https://shital.com), [Debadeepta Dey](www.debadeepta.com), [Gustavo de Rosa](https://www.microsoft.com/en-us/research/people/gderosa), Caio Mendes, [Piero Kauffmann](https://www.microsoft.com/en-us/research/people/pkauffmann/), and [Ofer Dekel](https://www.microsoft.com/en-us/research/people/oferd) at Microsoft Research.
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### Credits
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Archai builds on several open-source codebases. These includes: [Fast AutoAugment](https://github.com/kakaobrain/fast-autoaugment), [pt.darts](https://github.com/khanrc/pt.darts), [DARTS-PyTorch](https://github.com/dragen1860/DARTS-PyTorch), [DARTS](https://github.com/quark0/darts), [petridishnn](https://github.com/microsoft/petridishnn), [PyTorch CIFAR-10 Models](https://github.com/huyvnphan/PyTorch-CIFAR10), [NVidia DeepLearning Examples](https://github.com/NVIDIA/DeepLearningExamples), [PyTorch Warmup Scheduler](https://github.com/ildoonet/pytorch-gradual-warmup-lr), [NAS Evaluation is Frustratingly Hard](https://github.com/antoyang/NAS-Benchmark), [NASBench-PyTorch](https://github.com/romulus0914/NASBench-PyTorch).
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Please see `install_requires` section in [setup.py](https://github.com/microsoft/archai/blob/master/setup.py) for up-to-date dependencies list. If you feel credit to any material is missing, please let us know by filing an [issue](https://github.com/microsoft/archai/issues).
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### License
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This project is released under the MIT License. Please review the [file](https://github.com/microsoft/archai/blob/master/LICENSE) for more details.
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### Trademark
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This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
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### License
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This project is released under the MIT License. Please review the [file](https://github.com/microsoft/archai/blob/master/LICENSE) for more details.
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